text: import glob
  
  
  
  import matplotlib
  import matplotlib.pyplot as plt
  
  
  
  plt.style.use('seaborn')
  
  
  
  import numpy as np
  
  
  
  from addax.kavosh.enumerate import CreateDirectoryStructure
  from addax.utilities.dataIO import ReadGraph
  
  
  
  def PrintRunningTimeStatistics(graph, input_filename, motif_size, vertex_colored = False, edge_colored = False, community_based = False):
      """
      Print statistics for the running times for each vertex in the graph
  
      @param: graph: the input graph to evaluate
      @param input_filename: location for the graph that was enumerated
      @parak motif_size: the motif subgraph size to find
      @param vertex_colored: a boolean flag to allow for vertex colors
      @param edge_colored: a boolean flag to allow for edge colors
      @param community_based: a boolean flag to only enumerate subgraphs in the same community
      """
      # get the temp directory
      temp_directory = CreateDirectoryStructure(input_filename, vertex_colored, edge_colored, community_based, False)
  
      # get a list of all the input filenames for this motif size
      filenames = sorted(glob.glob('{}/certificates/motif-size-{:03d}-*.txt'.format(temp_directory, motif_size)))
  
      # create a dictionary of running times
      running_times = {}
      subgraphs = {}
  
      # keep track of the cpu time for each job
      cpu_times = []
  
      # read all of the filenames corresponding to enumeration data
      for filename in filenames:
          # keep track of the processor time
          cpu_time = 0.0
  
          # open the file
          with open(filename, 'r') as fd:
              # only worry about the lines with Enumerated summaries
              for line in fd.readlines():
                  if not line.startswith('Enumerated'): continue
  
                  # get the relevant stats from this line
                  segments = line.split()
                  nsubgraphs, vertex_index, time = int(segments[1]), int(segments[5]), float(segments[7])
  
                  # add to the mapping of running times
                  running_times[vertex_index] = time
                  subgraphs[vertex_index] = nsubgraphs
  
                  # update the time with this subgraph
                  cpu_time += time
  
          # append to the list of job times
          cpu_times.append(cpu_time)
  
      wall_time = max(cpu_times)
      idle_cpu_time = 0.0
      for cpu_time in cpu_times:
          idle_cpu_time += (wall_time - cpu_time)
  
      # make sure that every vertex is accounted for
      assert (sorted(list(running_times.keys())) == sorted(list(graph.vertices.keys())))
  
      print ('Running Time Statistics for {} Motif Size {}'.format(graph.prefix, motif_size))
      print ('  Mean Time: {:0.2f} seconds'.format(np.mean(list(running_times.values()))))
      print ('  Median Time: {:0.2f} seconds'.format(np.median(list(running_times.values()))))
      print ('  Maximum Time: {:0.2f} seconds'.format(np.max(list(running_times.values()))))
      print ('  Std Dev: {:0.2f} seconds'.format(np.std(list(running_times.values()))))
      print ('  95th Percentile: {:0.2f} seconds'.format(np.percentile(list(running_times.values()), 95)))
      print ('  Wall Time: {:0.2f} minutes'.format(wall_time / 60))
      print ('  Idle CPU Time: {:0.2f} hours'.format(idle_cpu_time / 3600))
      print ('  Total Time: {:0.2f} seconds'.format(sum(list(running_times.values()))))
  
      return running_times, subgraphs
  
  
  
  def EvaluateRunningTimes(input_filename, motif_size, vertex_colored = False, edge_colored = False, community_based = False):
      """
      Print statistics for the running times for each vertex in the graph
  
      @param input_filename: location for the graph that was enumerated
      @parak motif_size: the motif subgraph size to find
      @param vertex_colored: a boolean flag to allow for vertex colors
      @param edge_colored: a boolean flag to allow for edge colors
      @param community_based: a boolean flag to only enumerate subgraphs in the same community
      """
      # read the input graph
      graph = ReadGraph(input_filename, vertices_only = True)
  
      running_times, subgraphs = PrintRunningTimeStatistics(graph, input_filename, motif_size, vertex_colored = False, edge_colored = False, community_based = False)
  
      # create a figure for this statistic
      fig = plt.figure(figsize=(10, 5))
  
      # create the x and y variables
      x = []
      y = []
  
      # iterate over all variables
      for vertex_index in running_times.keys():
          x.append(subgraphs[vertex_index])
          y.append(running_times[vertex_index])
  
  
      # create an axis for the figure
      ax = fig.add_subplot(111)
  
      # plot the attribute
      ax.scatter(x, y)
  
      # set the titles
      ax.set_title('Running Times and No. Subgraphs', fontsize=20)
      ax.set_xlabel('No. Subgraphs', fontsize=18)
      ax.set_ylabel('Time (s)', fontsize=18)
  
      fig.tight_layout()
  
      plt.show()
  
      plt.clf()
  